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Customer Feedback Analysis – A Natural Language Processing Approach

Topic:Data Analytics & Visualisation, Others

Course Type:Short & Modular Courses

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Overview

  • Course Date:

    TBA
  • Registration Period:

    TBA
  • Time:

    TBA
  • Mode of Training:

    -
  • Venue:

    -
  • Funding:

    -

*Please note that once the maximum class size is reached, the online registration will be closed. You may register your interest and be notified if there is a new run.


Course Objective

Customer feedback serves as important sources of information for the businesses and organisations to reassess, re-plan and refine their products and services.

The proposed course aims to impart essential ideas, concepts and skillsets in Natural Language Processing with an aim to applying these skills to analyse customer feedback data.

In this course, participants will learn to apply Natural Language Processing (NLP) techniques and tools in Python to derive useful insights from text-based customer feedback data. They will learn basic text pre-processing and apply this to recognize patterns in customer reviews and derive actionable insights through sentiment analysis and text summarization. Participants will also have the opportunity to apply the skillsets to a real data set, through a mini-project, towards the end of the course.

Course Outline

By the end of the course, participants will be able to: 
• Apply NLP techniques such as n-gram analysis, text visualisation using word cloud, and topic-modelling to identify key topics and derive hidden patterns from customer reviews
• Apply sentiment analysis and text summarisation to extract actionable insights from customer reviews

Topics to be covered

1. Introduction to Customer Feedback Analysis with NLP
2. Basics of NLP – Text Pre-processing with Python
3. Recognising Patterns in Customer Reviews
4. Actionable Insights through Sentiment Analysis and Text Summarisation

 

Intel logo

With support from Intel, selected content from Intel® Digital Readiness Program material is included in this short course as supplementary content to enhance the relevance of this course to the Industry and to better support learners in appreciation of the applicability of the concepts covered.

 

Minimum Requirements

Basic knowledge about statistics and programming

Certification / Accreditation


• Certificate of Attendance (electronic Certificate will be issued)
A Certificate of Attendance will be awarded to participants who meet at least 75% attendance rate

• Certificate of Completion (electronic Certificate will be issued)
A Certificate of Completion will be awarded to participants who pass the assessment and meet at least 75% attendance rate

Suitable for

Data Analyst / Associate Data Engineer / Business Intelligence Manager / Business Intelligence Director

Course Fees

The course fees payable above are inclusive of 9% GST rate.

Applicants/Eligibility SkillsFuture Funding GST* Subsidised Fee (after GST)
Singapore Citizens aged 40 and above¹ $603.00 $18.09 $85.09
Singapore Citizens aged below 40 $469.00 $18.09 $219.09
Singapore Permanent Residents and LTVP+ Holders $469.00 $18.09 $219.09
SME-sponsored Singapore Citizens, Permanent Residents and LTVP+ Holders² $603.00 $18.09 $85.09
Others (Full fees payable) $0.00 $60.30 $730.30

As per SSG’s policy, the GST payable for all funding-eligible applicants is calculated based on prevailing GST rate after baseline funding subsidy of 70%

Singaporeans aged 25 years and above may use **SkillsFuture Credit balance to offset respective course fees.

¹ Under the SkillsFuture Mid-career Enhanced Subsidy. For more information, visit the SkillsFuture website here
² Under the Enhanced Training Support for Small & Medium Enterprises (SMEs) Scheme. For more information of the scheme, click here. To view SP’s list of similar funded courses, click here. Please submit the attached “Declaration Form for Enhanced Training Support Scheme for SME” together with your online application.


Funding Incentives

Please click here for more information on funding incentives.


Application Procedure

1. Application must be made through STEP. 

2. All successful applicants will be notified with a letter of confirmation via email.
 

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